Accurate predictions
about the quantity of products that will be returned under warranty can
provide huge benefits to manufacturing organizations. Among other
advantages, better warranty data analysis allows an organization to make the
most efficient allocation of resources to warranty services provision.
Likewise, they allow the manufacturer to anticipate customer support needs
and take the necessary steps to insure customer satisfaction with the
warranty process. Warranty data analysis can also provide a valuable
early-warning signal to the manufacturer when there is a serious product
quality problem in the field, which gives the organization time to mobilize
its resources to meet the challenge before serious financial, legal or other
problems occur. This article presents the process on how warranty analysis
can be accomplished in
Weibull++ 6.

The Warranty Analysis utility
that is available in Weibull++ 6 allows you to quickly and easily convert
shipping and warranty return data into the standard reliability data form of
failures and suspensions so that it can be easily analyzed with traditional
life data analysis methods. The utility uses the life data to generate
predictions about the quantity of warranty returns that can be expected in
the future. The following examples illustrate the principles upon which this
utility is based.

Data
Requirements

Shipping
and warranty return quantities are the minimum data requirements for
performing effective warranty data analysis. If an organization keeps track
of the quantity of units that are shipped in each given time period (e.g.
month) and the quantity of units from that shipment period that are returned
in subsequent time periods, life data analyses (including failure
predictions) can be performed. For each time period that elapses after the
units are shipped, count the number of returns (failures) and calculate the
number of units from the shipment that remain in the field (suspensions).
The data can be organized in a diagonal chart like the one shown in Figure
1.

Figure 1: Shipment and
returns data in Weibull++ 6.

Example 1:
Generating Life Data

Suppose that your
company keeps track of its shipments and warranty returns on a
month-by-month basis. The table in Figure 1 shows shipments in June, July
and August and warranty returns from July through September.

To convert this information to life data,
you must examine the companys shipments and returns on a month-by-month
basis. Out of 100 units shipped in June, 3 were returned in July. This is 3
failures at 1 month from the June shipment (FJUN,1 = 3).
Likewise, 3 failures from the June shipment occurred in August (FJUN,2
= 3) and 5 in September (FJUN,3 = 5). At the end of the
three-month analysis period, 11 units were returned and 89 units were still
in the field. Those 89 units are considered to be suspensions at three
months (SJUN,3 = 89). For the 140 units shipped in July, the
following failures and suspensions are observed: FJUL,1 = 2, FJUL,2
= 4 and SJUL,2 = 134. For the final shipment of 150 in August, 4
failed in September (FAUG,1 = 4) with the remaining 146 units
considered to be suspensions at 1 month of operation (SAUG,1 =
146).

To obtain a reliability data set, you must
add the quantity of failures and suspensions for each month, as shown next:

To generate this data set with the
Weibull++ Warranty Analysis utility, click the Create Weibull Data
button to generate the results shown in Figure 2. This data set can be
transferred to the Weibull++ Data Folio and analyzed. Using MLE analysis for
a two-parameter Weibull distribution, the parameter estimates are: Beta =
2.49 and Eta = 6.70.

Figure 2: Reliability
data generated in Weibull++ 6.

Example 2:
Making Warranty Predictions

Once you have
performed a life data analysis on warranty data, you can use the results to
predict the quantity of warranty returns you can expect in subsequent time
periods. Using the concept of conditional reliability, you can calculate the
probability of failure for the remaining units after each shipment time
period. Next, you can multiply this probability of failure by the number of
units from that shipment period that remain in the field in order to predict
the number of failures or warranty returns in the next time period.

Using the analysis performed in Example 1,
you can determine the conditional probability of failure for each shipment
time period and apply that probability to the number of units that were
still operating at the end of September. The equation of the conditional
probability of failure is:

For the June shipment, 89 units had not
been returned by the end of September. The probability of one of these units
failing in the next month is:

This value is multiplied by SJUN,3
= 89 to determine the number of failures or:

or 12 units.

Therefore, the forecasted number of returns
for October from the June shipment is 12 units. Predictions for the quantity
of returns that can be expected in October from the July and August
shipments can be performed using a similar methodology. The forecasts
generated in Weibull++ are presented in Figure 3.